Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs

Martorell, Sebastián; Sánchez, Ana Isabel; Alberola, Sofía Carlos; Serradell, V.

doi:10.1016/s0306-4549(01)00037-8

Cited by 54 publications

(30 citation statements)

References 20 publications

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“…This makes of single-objective optimization a well studied field in maintenance literature, though when looking to real-life cases multi-objective optimization is necessary. Some research has been done in the field of multi-objective maintenance optimization (Bucher and Frangopol 2006;Ilgin and Tunali 2007;Martorell et al 2002;Okasha and Frangopol 2009), but these always take into account the same optimization criteria. These are cost rate, total cost, availability and reliability.…”

Section: Maintenance Optimization Criteriamentioning

confidence: 99%

“…Certainly when looking to the integration of maintenance optimization models into decision making in industry. When looking at multiobjective maintenance optimization some research has already been done (Bucher and Frangopol 2006;Ilgin and Tunali 2007;Martorell et al 2002). But this is still limited to optimizing certain criteria (cost, availability, reliability) while these are not valid or important in certain industrial cases or do not take into account all business specific objectives.…”

Section: Introductionmentioning

confidence: 99%

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Maintenance optimization models and criteria

Horenbeek

Pintelon

Muchiri

2010

Int J Syst Assur Eng Manag

114

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Due to widespread automation and the high capital tied up in production equipment, the importance of maintenance is ever increasing. This makes maintenance an investment opportunity to be optimized, not a cost to be minimized. Academics have recognized this and many maintenance optimization models have been published over the years. Most of these models focus on one optimization criterion or objective, making multi-objective optimization models an underexplored area of maintenance optimization. Moreover, there is a big gap between academic models and application in practice. It is very difficult for industrial companies to adapt these models to their specific business context. This article reviews the literature on maintenance optimization models, with special focus on the optimization criteria and objectives used. To overcome flaws in present optimization models, a generic classification framework of maintenance optimization models is presented. All factors that have an influence on the optimization model will be made explicit and their links will be established. The framework is a starting point to develop business specific optimization models and enables decision making in e-maintenance. Moreover, it ensures a fit between the business model of a company and the maintenance optimization model right from the beginning. Future research will be on the development of a maintenance optimization model taking into account the most relevant optimization influence factors and criteria for a situation at hand.

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Section: Maintenance Optimization Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maintenance optimization models and criteria

Horenbeek

Pintelon

Muchiri

2010

Int J Syst Assur Eng Manag

114

View full text Add to dashboard Cite

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“…PRA uses analytical and time-independent models that need extension to account explicitly for the effects of testing and maintenance activities (Martorell et al 2002).…”

Section: Quantification Models Of Rams+cmentioning

confidence: 99%

“…In addition, averaged or yearly cost contributions are obtained based on analytical models of dependability (Harunuzzaman and Aldemir 1996, Busacca et al 2001, Martorell et al 2002, Vinod and Kushwaha 2004.…”

Section: Quantification Models Of Rams+cmentioning

confidence: 99%

Genetic Algorithm Applications in Surveillance and Maintenance Optimization

Martorell

Alberola

Sancho

et al. 2007

Computational Intelligence in Reliability Engineering

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“…Reference [16] presents a proposal of simultaneous optimization of parameters related to risk-based test and maintenance and functions of cost, modeled through genetic algorithms in the system level. The work presents an example of application of the methodology for the highpressure injection system.…”

Section: Introductionmentioning

confidence: 99%

Risk-Based Allowed Outage Time and Surveillance Test Interval Extensions for Angra 1

Gibelli

Melo

Leite

2012

International Journal of Quality, Statistics, and Reliability

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In this work, Probabilistic Safety Assessment (PSA) is used to evaluate Allowed Outage Times (AOT) and Surveillance Test Intervals (STI) extensions for three Angra 1 nuclear power plant safety systems. The interest in such an analysis lies on the fact that PSA comprises a risk-based tool for safety evaluation and has been increasingly applied to support both the regulatory and the operational decision-making processes. Regarding Angra 1, among other applications, PSA is meant to be an additional method that can be used by the utility to justify Technical Specification relaxation to the Brazilian regulatory body. The risk measure used in this work is the Core Damage Frequency, obtained from the Angra 1 Level 1 PSA study. AOT and STI extensions are evaluated for the Safety Injection, Service Water and Auxiliary Feedwater Systems using the SAPHIRE code. In order to compensate for the risk increase caused by the extensions, compensatory measures as (1) test of redundant train prior to entering maintenance and (2) staggered test strategy are proposed. Results have shown that the proposed AOT extensions are acceptable for two of the systems with the implementation of compensatory measures whereas STI extensions are acceptable for all three systems.

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Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs

Cited by 54 publications

References 20 publications

Maintenance optimization models and criteria

Maintenance optimization models and criteria

Genetic Algorithm Applications in Surveillance and Maintenance Optimization

Risk-Based Allowed Outage Time and Surveillance Test Interval Extensions for Angra 1

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